Advances in machine learning have enabled computing devices to solve complex problems in many fields. For example, image analysis (e.g., face recognition), natural language processing, and many other fields have benefitted from the use of machine learning techniques. For certain types of problems, advanced computing techniques, such as genetic algorithms, can be used to generate a machine learning model, such as a neural network. In one example, a genetic algorithm applies neuroevolutionary techniques over multiple epochs to evolve candidate neural networks to model a training data set.
Neural networks generally do not describe a human-understandable relationship between input data and output data. Stated another way, it is generally not clear, from a human perspective, whether or why a specific neural network would be expected to produce a reliable result. Accordingly, it can be challenging to determine whether a particular candidate neural network that is produced by the genetic algorithm is likely or unlikely to be accurate or reliable. The accuracy and/or reliability of a neural network can be summarized using a fitness value, which indicates how closely output of the neural network matches an expected output determined based on the training data set. However, determining a fitness value for each neural network during each epoch of a neuroevolutionary process is time consuming and uses significant processing resources.